Srichart 2016-The SEACEN Centre-Determinants of MP transmission via bank lending channel in Thailand-A Threshold Vector Autoregression approach

Chapter DETERMINANTS OF MONETARY POLICY TRANSMISSION VIA BANK LENDING CHANNEL IN THAILAND: A THRESHOLD VECTOR AUTOREGRESSION APPROACH1 By Kantapon Srichart2 Kongphop Wongkaew3 Suchanan Chunanantatham4 Sukjai Wongwaisiriwat5 Introduction “Most economists would agree that, at least in the short-run, monetary policy can significantly influence the course of the real economy There is far less agreement, however, about exactly how monetary policy exerts its influence.” Excerpt from “Inside the Black Box: The Credit Channel of Monetary Policy Transmission,” Bernanke and Gertler (1995) We have come a long way toward unraveling the black box on monetary transmission mechanism Since the theoretical underpinnings of various channels have been found, an extensive sum of empirical researches have shed some light on what happen in the interim from changes in monetary policy to changes in output and inflation In light of Thailand experience, the empirical results point to a transmission mechanism in which banks play an important role, through the adjustment of both price and quality of loans, relative to exchange rate and asset price channel Disyatat and Vongsinsirikul (2002) argue that the traditional interest rate channel accounts for around half of output The views expressed in this paper are of the authors and not reflect those of the Bank of Thailand, its executives or The SEACEN Centre All errors and opinions expressed in this paper are sole responsibility of the authors Economist of the Macroeconomic and Monetary Policy Department of the Bank of Thailand Economist of the Macroeconomic and Monetary Policy Department of the Bank of Thailand Senior Economist of the Macroeconomic and Monetary Policy Department of the Bank of Thailand Economist of the Macroeconomic and Monetary Policy Department of the Bank of Thailand 233 response after years while Charoenseang and Manakit (2007) show that shocks to policy rate increase private credits significantly for about month, which in turn help stimulate output mainly through private investment Consequently, given the economy’s heavy reliance on the banking sector, monetary policy effectiveness is believed to depend largely on commercial banks’ rate adjustment as well as sensitivity of credits and deposits following changes in policy rate in Thailand In a changing economy, the channels of monetary transmission are unlikely to be constant over time According to the preliminary studies done for recent policy easing cycle, the sensitivity of retail rates to money market rates’ reduction appears to decline, thereby suggesting a weakening interest rate pass-through after 2010 Meanwhile, monetary easing in Thailand seems to have less influence in boosting bank loan in the current credit decelerating trend Therefore, in order to continuously ensure appropriate design and successful conduct of monetary policy, it is of great importance to be alerted of the impact of changes that alter the economic effects of given monetary policy measures The main objective of this paper is thus to revisit the transmission via banking sector and identify the determinants behind those changes for Thai economy While there are studies that look into the influences of bank friction on monetary policy effectiveness both theoretical and empirical6, this paper’s aim is to test the effect of the boarder economic landscape on monetary policy effectiveness Motivated by the current state of economy, we ask whether monetary policy is effective in an economic downturn period Intuitively, the initial economic condition determines where we are on the aggregate supply curve and how large is the aggregate demand shift as a result of a monetary policy shock, hence the change in equilibrium output A shift in aggregate demand could be larger when the economy is below par and firms are underleveraged but this trend could be offset by the effect of worsening business confidence On the other hand, in an economic downturn phase, when there are large amounts of spare capacity available, the aggregate supply curve is expected to be very elastic Hence, the effect of monetary easing on output is expected to be higher With the above hypothesis in mind, we ask whether/how the impact of monetary policy on macroeconomic dynamics changes with the phase of business cycle for Thailand To conduct an empirical exercise, the threshold vector autoregression (TVAR) methodology is employed as it is appropriate for modeling regime shifts, i.e., shift between subpar and above par GDP regime Our results Including Disyatat (2010), Gambarcorta and Marques-Ibanez (2011) and Ananchotikul and Seneviratne (2015) 234 indicate that the dynamics of the interactions among credit market condition, economic activities, and monetary policy seems to change as the economy moves from a subpar growth regime to an above-par regime Although credit growth tends to show smaller response to monetary policy easing, possibly due to subdued private sector confidence, output response seems to be higher during a downturn when the economy is more likely to be have low capacity utilization To set stage for our discussion on monetary policy transmission, we begin by reviewing the conceptual framework of how transmission channels via banks could change with the phase of the business cycle in Section Section contains a brief overview/stylized facts on the transmission mechanism in Thailand The methodology and database are presented in Section 4, followed by the empirical results from the TVAR analysis in Section Section concludes and the technical details are presented in the appendices Literature Review and Conceptual Framework 2.1 Conceptual Framework and Theoretical Considerations Over the past decade, there are a growing number of literatures which seek to provide evidence that the effectiveness of monetary policy depends, among other factors, on the state of economic activities This section provides a simple framework for investigating the various theories underpinning this concept The merit of such a framework is that it allows us to bridge the arguments which rest on different assumptions and lines of reasoning suggested by each model with their following empirical results According to the traditional macroeconomic concept, the equilibrium of real output and the price level is determined by the intersection of the aggregate supply and the aggregate demand curves Monetary policy affects such equilibrium, via its influence on aggregate demand Monetary easing, for instance, lowers interbank financing costs, and commercial banks typically pass on the lower cost to their customers in terms of lower lending rates At the same time, as funding costs become lower, banks also tend to expand their loan supply As a result, private spending and aggregate output rise Nevertheless, there is empirical evidence which suggests that loan demand and supply might also depend on factors other than costs of funds The following section outlines the key determinants of loan demand and loan supply respectively Finally, after considering the equilibrium in the credit market – which determines the aggregate demand curve – and the curvature of the aggregate supply curve, we then move on to explain how monetary policy effectiveness varies with the state of economic activities 235 Transmission of monetary policy relies crucially on its role in influencing credit demand in three ways, as summarized in the following functional form Loan demand = f(lending rate, expectation on economic outlook, borrower’s balance sheet) Firstly, firms will increase their borrowing if the cost of funds falls below the internal rate of return In this sense, the traditional strand of monetary transmission contends that monetary easing reduces the firm’s cost of fund, which then induces aggregate demand However, such a conclusion rests importantly on the assumption that banks will pass on the lower cost to firms Secondly, firms’ demand for borrowing is positively correlated with their expectation on the outlook of the economy A bright economic prospect will prompt firms to acquire more credits to fund their investments This notion is supported by Kashyap, Stein, and Wilcox (1993) who provide evidence of a positive relationship between economic conditions and the demand for bank credit Nevertheless, it is important to stress that such a conclusion requires monetary policy to be sufficiently credible, so that the monetary easing action is perceived to contribute to a brighter growth prospect going forward In the absence of such credibility, the demand for loans may not be as responsive to the monetary stimulus Thirdly, firms’ demand for borrowing is also subject to the prevailing conditions of their balance sheets Highly-leveraged firms or firms with deteriorating balance sheet conditions tend to face limitations in their external financing In this respect, monetary easing can alleviate such tensions in their balance sheets, as a corresponding fall in the discount rate helps increase the net present value of firms’ assets The channel in which monetary policy exerts its influence on firm’s balance sheet is generally referred to as the ‘balance sheet channel,’ which is one of the two strands of the credit channel of transmission On the supply side, the key factors which determine bank loan supply are the following: Loan supply = f(external finance premium, expectation on borrower’s balance sheet, level of risk aversion) First of all, the financing condition of a financial intermediary has an influence on the supply of credit Monetary policy exerts influence on a bank’s external 236 funding cost by directly setting the policy rate, which in turn acts as short-term benchmark rates in the financial markets At the same time, monetary policy influences market expectation of future path of interest rate, which then affects the costs of longer-term financing In addition, monetary easing indirectly affects the default risk premium which banks face in tapping market financing due to its influence on banks’ balance sheets Monetary easing which pump up asset prices also improve banks’ net worth in the same way as the effects of the aforementioned effects on firms’ balance sheet Firms’ balance sheets also play a role in determining the provision of credit Bernanke and Gertler (1989) designed a model of business cycle with the inclusion of the role of firms’ balance sheets for highlighting this concept Assuming that a bank maximizes profit and has to deal with imperfect information of the borrowers, the expected net worth of a firm serves as a leading indicator of a borrower’s probability to default As a firm’s wealth deteriorates, adding to the possibility of a default, a bank may guard its wealth against such default risks by tightening the credit condition and vice-versa The key implication is that this mechanism becomes a source of pro-cyclicality, exacerbating the downturn and fueling the expansion Bayoumi and Melander (2008) developed the macrofinancial linkages and found significant evidence that credit conditions have influence on real spending Finally, loan supply may also vary with risk aversion of financial intermediaries which changes in response to business/economic outlook Kahneman and Tversky (1979) proposed the so-called prospect theory which argues that when economic agents become risk averse in an environment, consumption will fall below a habit-based reference level – a concept which could also help explain the behavior of banks The implication is that an economic recession usually concurs with some sort of confidence crisis, which further acts as a propagator of negative shocks to economic growth, delaying the recovery Putting together the factors affecting loan demand and supply would result in the equilibrium in the loan market This, in turn, determines the magnitude of the shift in the aggregate demand curve following a monetary easing action In the low-growth regime, for instance, if the sentiment factor dominates a fall in financing costs, then a shift in aggregate demand (AD) will be marginal However, in the absence of negative sentiment or uncertainty, a shift in AD will be relatively larger The shape of the aggregate supply curve also plays a role in determining the effectiveness of monetary policy Keynes is among the earlier supporters of 237 this argument which suggests that the aggregate supply (AS) curve is positivelysloped up to the expected price level and vertical afterwards as the economy reaches its long-run potential The Keynesian concept implies that monetary policy shocks in the state of high economic activity are neutral but those in a low-activity environment are effective, implying that monetary policy is more powerful in a state of low economic growth than in the period of expansion Related theories include the ‘costly price adjustment’ strand, cited in Tsiddon (1993), and Ball and Mankiw (1994) Ball and Mankiw (1994) proposed the socalled “Menu Cost” model which is derived on microeconomic foundations The model assumes that a single firm bears “the Menu Cost” of adjusting prices to maintain the relative price of its goods to the overall price, in a backdrop of continuing positive inflation The authors argue that a positive inflation rate helps offset a negative shock in overall prices, bringing the relative price back to its preferable level without needing any downward adjustment On the contrary, inflation acts as propagator of positive shock to the overall price and the firm has to raise its price even higher to shore up its relative price towards the desired level Thus, a firm is more likely to adjust their price upwards rather than downwards, with implications of a convex aggregate supply curve Based on this simple AD-AS framework, the resulting equilibrium output depends on two forces – the magnitude of shift in the AD curve, and the slope of the AS curve For instance, in a state of high economic activity, a monetary easing shock may shift AD significantly, but given the relatively steep AS curve, the effect on output would become smaller 2.2 Empirical Evidence 2.2.1 Reviews of Literatures on Monetary Policy Transmission in Thailand Literatures on transmission via the banking sector in Thailand are divided into two main strands The first strand of research concerns the quantitative assessment of the consequences of a change in the policy rate on macroeconomic variables and how they change over time The second strand focuses on the determinants of the transmission mechanism Finally, we also outline the key factors underlying the evolution of monetary transmission in the past decade Regarding traditional interest rate channel, the prominent view is that there was a significant decline in the pass-through from the policy rate to bank retail rates in Thailand following the East Asian financial crisis in 1997 Using the 238 Error Correction Model (ECM) analysis, Disyatat and Vongsinsirikul (2002) argued that the retail rates in Thailand is generally sticky to policy rate movement compared to those in developed countries and they became stickier in the aftermath of the crisis These results are consistent with Atchana and Singhachai (2008), whose work documents a decline in the responsiveness of retail rates to policy rate changes following the financial crisis, with stickiness of policy pass-through being most evident around 2004-2005 Also, Charoenseang and Manakit (2007) found that despite the observable long-run relationship between the policy rate and money market rates, the pass-through effect of the policy rate on banks’ retail rates is quite low, at about 20% during 2000-2006 The authors also estimated the vector autoregression (VAR) system on Thailand data during 2000-2006 and found that the policy rate does not strongly influence the lending rate, suggesting a weaker transmission through interest rate channel after the adoption of inflation targeting in 2000 According to the abovementioned literatures, level of competition and the liquidity in the banking sector are noted as the two main catalysts Disyatat and Vongsinsirikul (2002) contend that a cost of rate adjustment is higher in the less competitive banking sector than in a more competitive system In addition Atchana and Singhachai (2008) argue that the degree of risk aversion in the banking system has changed since the outbreak of the 1997 financial crisis, as bank reserves greater portion of cash and liquid assets in excess of the legal requirement Against this backdrop, marginal tightening in monetary policy would not be able to tempt banks to raise its lending rates Charoenseang and Manakit (2007) draw a similar conclusion on excess liquidity It was not until mid-2015 that the excess liquidity started to reduce, after which the interest rate passthrough began to pick up more evidently Most of the literatures on monetary transmission generally agree that the bank lending channel could help amplify the effect of interest rate shock beyond what would be predicted if the monetary policy were to transmit its effect through the interest rate channel alone According to Disyatat and Vongsinsirikul (2002), monetary tightening leads to a fall in bank credits with about quarters lag and bank loans also have significant implication on the impulse response of GDP from interest rate shocks Similarly, Charoenseang and Manakit (2007) found that shocks to monetary policy induced major changes in commercial banks credits to private sector for about months while commercial bank credits have strong impact on private investment However, there is a growing recognition that the significance of the credit channel and the importance of bank loans have declined since the crisis period 239 As argued by Disyatat and Vongsinsirikul (2002), the sensitivity of loan supply to monetary shocks has gone down since 1999, along with effectiveness of monetary policy associated with the bank lending channel By comparing the VAR of the whole sample and truncated data of up to 1999, the paper finds that the response of output and bank credits to monetary policy of a similar size is more pronounced in the pre-crisis period The authors argued that this is attributed to a rise in prominence of non-deposit funding for banks, which serve as a cushion against a tightening of monetary policy, in turn reducing the sensitivity of loan supply and output to monetary shocks Also, a firm can substitute nonbank financing for bank loans when monetary policy tightens In addition, Disyatat and Vongsinsirikul (2002) also focused on the financial health of the banking and corporate sector which affects how monetary shock is translated into bank credit, the chief motivation of our study By effectively constraining new bank lending, a continued weakness in the banking sector following the crisis, tended to offset the impact of monetary easing At the same time, excess capacity and balance sheet weakness in the corporate sector also act as a constraint on investment demand, thereby blunting the credit channel of monetary policy We will elaborate more on this argument Nonetheless, there are also a few literatures, providing evidences in favor of an improved bank lending channel Amarase and Rungcharoenkitkul (2014) offers a model to support the fact that greater bank competition and lower riskfree rate raise the screening costs, eventually leading to a pooling equilibrium involving larger credits at cheaper prices In context of the Thai experience, a shift in Specialized Financial Institutions’ (SFIs) lending strategy may have triggered a transition of equilibrium from credit rationing to credit boom As competition and risk-taking intensified during the 2011-2013 easing episode, banks strategically increased credit supply, as reflected by a compressed spread Therefore, bank competition can play an important part in strengthening the impact of monetary policy on bank lending and economy during the current easing cycle In sum, based on literature of the Thai experience, banks are still central elements in monetary policy transmission mechanism Nevertheless, its relevance has declined mainly through the price perspective On top of the monetary policy framework which should influence the degree of transmission, the literature also point to (i) excess liquidity and competition in banking sector; (ii) financial deepening; and, (iii) financial health of banks 240 2.2.2 Evidence of Non-linear Monetary Policy Influence on Real Output Many literatures confirm the non-linear interaction between monetary policy and real output with regard to a state of economy In the case of developed countries, the earlier study of Garcia and Schaller (2002) examined the goodness of fit of the Markov-switching model which treats the state of economy as a latent variable versus the linear model in simulating the response of output to policy rate Their results confirm the existence of the asymmetry regarding the economic environment Lo and Piger (2003) also deploy VAR analysis on the US data during 1954Q3 to 2002Q4 and find strong evidence of time variation in the relationship between monetary policy and output Regressing the probabilities of change in this relationship on several state variables, the authors find strong evidence that regime shifts can be well explained by the phase of the business cycle The study, however, finds no strong evidence in favor of asymmetry with regard to the direction of policy action and does not test whether policy direction matters within each growth regime Some of the literatures adopt the threshold vector autoregression (TVAR) model, including Balke (2000) who tested the two-regime switching model and Avdjiev and Zeng (2014) who developed a three-regime switching model in similar spirit to Balke (2000) Both studies corroborate the existence of the asymmetry Other papers include Weise (1999), and Thoma (1994) Using U.S data, empirical literatures show mixed results The first group favors the argument for more potent monetary policy in a state of low-economic growth than those in high growth periods, namely Weise (1999), Balke (2000), Garcia and Schaller (2002), and Lo and Piger (2003) and Avdjiev and Zeng (2014) Estimations deployed in Garcia and Schaller (2002) affirms that the effect of monetary tightening on output is more powerful during recessions than during expansions According to the credit-rationing proposition, Balke (2000) finds that monetary tightening shocks are more potent in the tight-credit environment which is concurrent with the state of subdued economic activity and confidence So Avdjiev and Zeng (2014), who argue that monetary easing is more effective when economic agents are under credit constraint than when the agents are already fully financed Note that the nature of asymmetry with regard to a state of economy depends on whether monetary policy action is expansionary or contractionary 241 On the other hand, there is also evidence supporting the claim that monetary tightening is more effective in the low-growth regime Thoma (1994) finds that monetary tightening has a stronger adverse effect on output which is significant during the three to five quarters after the policy action is taken On the contrary, contractionary policy has no significant effect during recessions Monetary policy is also found more potent in a state of high growth rates by Tenreyro and Thwaites (2015), consistent with the “pushing on the string” concept In the case of the Asian economies, there are mixed evidences on both the existence of non-linearity and in which regime monetary policy is more powerful Hooi et al (2008) employed a Generalized Hamilton Markov switching model in the same spirit as the prior work of Garcia and Schaller (2002) Utilizing quarterly data of Indonesia, Malaysia, Philippines and Thailand during 1974Q1 to 2003Q1, the results confirm the existence of asymmetry with respect to a state of economy and shows that monetary policy has larger effects on output during expansions Shen (2000) applied a time-varying asymmetric model on Chinese Taipei data and failed to reject the linearity of a relationship between monetary policy and output However, the point estimates imply that monetary tightening is more effective during the contraction and confirms the hypothesis of credit-rationing Overview of Thailand’s Monetary Policy and its Transmission This section aims to provide stylized facts on how the dynamics between credit, economic activities, and monetary policy should interact during the period of economic downturn in Thailand By analyzing a set of selected variables according to the conceptual framework laid out in second section, we will attempt to provide an analysis regarding the size of the aggregate demand shift and slope of the aggregate supply curve which should serve as a initial evidence on how credit conditions and eventually economic activities should change in response to monetary easing in a period of economic slump in Thailand Simply put, this section serves as a qualitative review of the effectiveness of monetary easing in Thailand, before proceeding to the quantitative results from the TVAR approach in the following sections 3.1 Aggregate Demand Curve and Credit Market Condition As described in last section, the equilibrium credit and the size of shift in the AD curve is determined by both interest rates, i.e., external finance premium (EFP), and the sentiment of economic agents regarding economic outlook 242 Impulse Response Function (GIRF) is the response of a specific variable after a one-time shock hits the forecast of the variables in the model Firstly, we estimate the GIRF as follows: (2) where Ωt-1 is the past information set at time t – and ut is a particular realization of the exogenous shock Typically, the effect of a single exogenous shock is examined at a time, so that value of the ith element in ut , uti is set to a specific value The difficulty arises because, in the TVAR, the moving-average representation is not linear in the shocks (either across shocks or across time) As a result, unlike linear models, the impulse-response function for the nonlinear model is conditional on the entire past history of the variables and the size and direction of the shock The conditional expectations of Yt+k are calculated by simulating the model using randomly drawn shocks To compute E [Yt+k|Ωt-1], we use the random sample ut+k by taking the bootstrap sample from the estimated model residual, ut We repeat the simulation for –ut+j in order to eliminate any asymmetry that might arise from sampling variation in the draws of ut+j This is repeated 5,000 times, and the resulting average is the estimated conditional expectation Empirical Results Based on the methodology outlined in the previous section, the estimated threshold of real GDP growth is 3.27% (year-on-year) Such a threshold essentially separates the observations into two regimes, henceforth called the high-growth regime and the low-growth regime In this paper, our focus is on analyzing the impacts of monetary easing on three key macro variables: real GDP growth, headline inflation, and real credit growth The following section reports the responses of each variable under the two growth regimes, following a one-time monetary shock As the responses are symmetric, we will only report the impacts of a monetary easing action, which seems more relevant given the current situation in Thailand Finally, consistent with the literature of other economies, we expect monetary easing to have a larger impact on the real variables in the low-growth regime than in the highgrowth regime Details of the estimated equations are provided in Appendix B 248 5.1 Responses of Real GDP Growth In both regimes, real GDP growth responds positively to monetary easing, which in this case, is a one standard deviation (one-SD) shock in the policy interest rate However, as seen in Figure 7, the magnitude of the response is higher in the low-growth regime than in the high-growth regime In the lowgrowth regime, the response of real GDP growth peaks at around 0.28 SD (equivalent to 0.98% yoy), one quarter after the policy rate cut, while the peak is only 0.08 SD (0.28% yoy) in the high-growth regime In both regimes, the effects of the shock die down at around the eighth quarter, after which the responses turn slightly negative In short, monetary easing seems to be more effective in raising output when the economy is in a low-growth regime than in a high-growth one – in line with our expectation Nevertheless, the swift reaction of output to monetary shocks remains puzzling, particularly in contrast with the conventional notion that monetary policy typically has a lag of around 6-8 quarters 5.2 Responses of Headline Inflation In both regimes, headline inflation responds positively to monetary easing No price puzzle is detected in the 35-month horizon investigated Similar to the responses of output, monetary easing raises inflation more when in the lowgrowth regime than in the high-growth one In the low-growth regime, the response of inflation peaks at around 0.16 SD (equivalent to 0.31% yoy), while the magnitude is halved in the high-growth regime In both regimes, the peaked responses of inflation occur approximately two quarters after the shock Regarding the persistence of the responses, the effects of the shock on inflation are virtually zero after twelve quarters 5.3 Responses of Bank Credit Overall, bank credit responds positively to monetary easing In the lowgrowth regime, however, there is credit puzzle during the first three quarters, when bank credit falls and bottoms out after the first quarter From Figure 7, it can be seen that bank credit responds more to monetary easing when in the low-growth regime than in the high-growth one, with the peak responses of around 0.27 SD (equivalent to 2.24% yoy) and 0.18 SD (1.51% yoy) respectively In both regimes, the effects of monetary easing on bank credit gradually die down but remain fairly sizable even at the end of the 35-month horizon 249 Figure Responses of Real Variables to a One-SD Negative Monetary Shock Source: Authors’ calculations Figure Economic Growth and Detrended Bank Capital Source: Bank of Thailand, authors’ calculations 250 In an attempt to explain the different responses of bank credit in the two regimes, we investigated the role of bank capital in influencing the credit supply, by using capital as a threshold variable instead of real GDP growth At the same time, bank capital is included as an endogenous variable in the VAR system in order to investigate its role as a shock propagator In essence, this exercise allows us to track the evolution of bank credit after its capital is affected by monetary easing In undertaking such an exercise, we opt for the de-trended capital ratio rather than the level of bank capital itself9, as the latter is nonstationary and trends with economic growth over time Therefore, removing its trend allows us to observe, in a more meaningful way, how bank capital evolves with the business cycle, on top of banks’ own discretion on capital holding At the same time, this manipulation allows us to observe the interaction between bank capital and the state of economic activities Indeed, a basic plot of real GDP growth and de-trended bank capital in Figure shows that the two series are fairly correlated, particularly in the aftermath of the Global Financial Crisis in 2008 Comparing the two charts on the left-hand-side of Figure 9, it is obvious that bank capital responds differently to monetary easing, depending on the initial condition of capital In a low-capital regime10, bank capital initially falls following a negative monetary shock, whereas in a high-capital regime bank capital responds positively A fall in bank capital during the first two quarters helps explain the credit puzzle in the bottom right chart in Figure Henceforth, this de-trended bank capital will be referred to as ‘bank capital’ for simplicity’s sake 10 Following the same methodology as the GDP exercise, the estimated threshold for detrended capital is -0.22% (yoy) 251 Figure Responses of Bank Capital and Credit to Monetary Policy Shock Source: Authors’ calculations 5.4 Significance of Results As explained in the methodology section, several attempts have been made to improve the significance of the regression Exogenous variables such as the Industrial Production (IP) Index of the U.S and the dummy variable for the flooding incident are included in the final model specification as they are factors which likely affect domestic output but are beyond control of domestic monetary policy A number of other variables are also included, but seem to contribute only marginally to the overall significance of the regression Despite the aforementioned attempts, the explanatory power of the TVAR model remains fairly low for both regimes11 As seen in Figure 10, the standarderror bands are therefore wide compared to the mean of responses for all three real variables, particularly for bank credit This implies that the reported responses of real variables to monetary shocks are not statistically significant 11 See Appendix B for the estimated equations 252 Figure 10 Responses of Real Variables to a One-SD Negative Monetary Shock Source: Authors’ calculations Conclusion We have come a long way in unveiling the black box on monetary transmission mechanism In the case of Thailand, the empirical results point to a transmission mechanism in which banks play an important role, through the adjustment of both price and quality of loans, relative to the exchange rate and asset price channel However, according to the preliminary studies done for the recent policy easing cycle, the quantity of bank lending and hence output, may not be as responsive to monetary policy actions as the central bank desires Motivated by such a trend, the main objective of this paper is to identify the determinants behind those changes for the Thai economy In particular, this paper asks whether and how the impact of monetary policy on macroeconomic dynamic changes with the phase of the business cycle, that is whether monetary policy is still effective during the economic downturns Intuitively, the initial economic conditions determine where we are on the aggregate supply curve and how large aggregate demand shifts in response to a monetary policy shock, with the resulting change in the equilibrium output A shift in aggregate demand could be larger when economic growth is below par and firms are underleveraged but this could be offset by the effect of worsening business confidence On the other hand, in the downturn phase, when there is ample spare capacity, the aggregate supply curve is relatively elastic Hence, 253 the effect of monetary easing on output is expected to be higher than is the case during the boom times In conducting the empirical study to test the above hypothesis, the TVAR model with four endogenous variables, namely GDP growth, inflation, credit, and policy rate is adopted Our results, which are consistent with the stylized fact found for Thailand’s data, provide evidence that the dynamics of the interactions among credit market conditions, economic activities, and monetary policy is likely to change as the economy moves from subpar growth regime to above-par regime Although credit growth shows a smaller response to monetary policy easing during the initial period, possibly due to subdued private sector confidence, the output response seems to be higher during the downturn when the economy is more likely to have low capacity utilization At first glance, it might seem that our finding of greater effectiveness of monetary policy in the low-growth regime contradicts the anecdotal evidence of the recent sluggish recovery in Thailand However, it should, by no 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serving as absorption of losses potentially generated by risky assets, capital adequacy ratio is equal to aggregate capital funds divided by risk-weighted assets in percent The data covers those of commercial banks registered in Thailand and also foreign bank branches and publicly reported on a monthly basis Commercial banks are subject to Baselbased capital regulations Banks registered in Thailand are required to maintain the ratio not below 8.5% since 1988 while foreign bank branches are subject to a 8.5% minimum requirement since 2013, compared to 7.5% during August 1988 and December 2012 (Table FI_CB_030 and FI_CB_030_S2 Bank of Thailand Statistics) Real Credit Growth (RCREDYOY) Credit is defined as end-month outstanding amount of commercial banks credit to domestic Other Nonfinancial Corporations (ONFCs), households, and Nonprofit Institutions Serving Household Sector (NPISH), in accordance with the Monetary and Financial Statistical Manual (MFSM2000) Credit growth is in year-overyear basis and expressed in percentage (Table EC_MB_012 Bank of Thailand Statistics (Jan-2000 to Dec-2002) and commercial bank private credit data used internally by Monetary Policy Group of Bank of Thailand (Jan-2003 to Mar-2015) Real GDP Growth (RGDPYOY) Started with the quarterly dataset compiled by the National Economic and Social Development Board (NESDB), we constructed the monthly data of Gross Domestic Products by using interpolation to convert quarterly GDP to monthly data While, we proxy movement of monthly GDP each month with movements of Coincidence Economic Indicator The indicator is constructed from components including real imports, manufacturing production index, real gross value added tax, volume sales of automobiles and real debit to demand deposit GDP growth is on year-over-year basis and express in percentage (URL: http:/ /www.nesdb.go.th/Default.aspx?tabid=95) Policy Rate (POL) The policy rate is the rate that The Monetary Policy Committee announced to conduct monetary policy in Thailand under the inflation-targeting framework The 14-day repurchase rate (RP rate) was used as the policy interest rate up 258 until 16 January 2007, after which the policy interest rate was switched to the 1- day RP rate Since 12 February 2008, with the closure of the BOT- run RP market, this was switched to the 1-day bilateral RP rate Policy rate is on percent per annum basis and expressed in the end of the month (Table FM_RT_001 and FM_RT_001_S2 Bank of Thailand Statistics) Headline Inflation Rate (HLCPIYOY) The headline consumption price index dataset collected by Ministry of Commerce used as inflation because the Monetary Policy committee has agreed to propose new monetary policy target for 2015 The new target is set for the annual average of headline inflation in 2015 to be at 2.5 percent with a tolerance band of ± 1.5 percent Inflation rate is in year-over-year basis and expressed in percentage (URL: http://www.price.moc.go.th/ content1.aspx?cid=1) US Industrial production index (USIPIYOY) The US industrial production index measures the real output of all manufacturing, mining, and electric and gas utility establishments Because of Thailand is a small open economy, it is important for controlling external factors To distinguish the impact of policy rate to real GDP growth and headline inflation from global effects, US Industrial production is included as exogenous variable Thai Flooding Dummy Variable (DUMFLD) Thailand has experienced severe flooding in 2011 that impacts to sharp drop in manufacturing sector and slump Real GDP growth We applied the same way as US industrial production index variable by controlling other factor to influence monetary transmission mechanism It takes a value of for data since October 2011 to December 2011, and otherwise 259 Appendix B: Estimation Results Table 1: Estimation Results: Whole Sample Note: *, **, *** significant at 10%, 5% and 1% respectively Sources: Authors’ calculations 260 Table 2: Estimation Results: Subsample in High Growth Regime Note: *, **, *** significant at 10%, 5% and 1% respectively Sources: Authors’ calculations Table 3: Estimation Results: Subsample in Low Growth Regime Note: *, **, *** significant at 10%, 5% and 1% respectively Sources: Authors’ calculations 261 262 [...]... way in unveiling the black box on monetary transmission mechanism In the case of Thailand, the empirical results point to a transmission mechanism in which banks play an important role, through the adjustment of both price and quality of loans, relative to the exchange rate and asset price channel However, according to the preliminary studies done for the recent policy easing cycle, the quantity of bank. .. taken into account This is the essence of Section 5 where quantitative exercises are carried out to examine the overall effect of a monetary policy shock 244 3.2 Aggregate Supply Curve and the Equilibrium Output In addition to the size of shift in the AD curve, the slope of the AS curve is also vital in determining the output effect of monetary easing As shown in Figure 5, in the declining phase of the... and Headline Inflation Source: Bank of Thailand, Authors’ calculations 245 4 Empirical Methodology 4.1 Model Specification In this paper, the Threshold Vector Autoregression (TVAR) is used to explore the monetary policy transmission via the bank lending channel As opposed to a linear VAR model, the TVAR enables us to test whether the effectiveness of monetary policy varies with the prevailing macroeconomic... typically has a lag of around 6-8 quarters 5.2 Responses of Headline Inflation In both regimes, headline inflation responds positively to monetary easing No price puzzle is detected in the 35-month horizon investigated Similar to the responses of output, monetary easing raises inflation more when in the lowgrowth regime than in the high-growth one In the low-growth regime, the response of inflation peaks... end of the 35-month horizon 249 Figure 7 Responses of Real Variables to a One-SD Negative Monetary Shock Source: Authors’ calculations Figure 8 Economic Growth and Detrended Bank Capital Source: Bank of Thailand, authors’ calculations 250 In an attempt to explain the different responses of bank credit in the two regimes, we investigated the role of bank capital in influencing the credit supply, by using... response of bank net worth (proxied by bank capital) to positive a policy shock and the association negative relationship between bank net worth and EFP (Figure 2) could provide amplification for the effect of monetary easing on the amount of credit supply In other words, after monetary easing, banks’ net worth could increase, causing a decline in the EFP With lower cost of funds, banks are more willing... economic activities Indeed, a basic plot of real GDP growth and de-trended bank capital in Figure 8 shows that the two series are fairly correlated, particularly in the aftermath of the Global Financial Crisis in 2008 Comparing the two charts on the left-hand-side of Figure 9, it is obvious that bank capital responds differently to monetary easing, depending on the initial condition of capital In a low-capital... distinguish the impact of policy rate to real GDP growth and headline inflation from global effects, US Industrial production is included as exogenous variable Thai Flooding Dummy Variable (DUMFLD) Thailand has experienced severe flooding in 2011 that impacts to sharp drop in manufacturing sector and slump Real GDP growth We applied the same way as US industrial production index variable by controlling... monetary policy Finally, we use a similar ordering of variables in the VAR system akin to those of most standard VAR literatures that adopt a recursive structure With regard to the lag order selection, our objective is to strike a balance between minimizing the conventional information criterion and maintaining a sizable number of observations in each regime to ensure reliability of results In our case,.. .In the case of Thailand, in the period where GDP growth is subpar, the amount of credit could be highly responsive to monetary easing considering the possibility of reduction in EFP (proxied by probability of default for the Thai banking sector) As can be seen in Figure 1, the high level of EFP during the subpar growth implies a large space for reduction after monetary easing Furthermore,